Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
This addresses safety and robustness issues for AVs in mixed traffic, but it is incremental as it builds on existing MARL approaches with a novel reward mechanism.
The paper tackles the challenge of autonomous vehicles (AVs) cooperating with human-driven vehicles (HVs) in mixed traffic by framing it as a multi-agent reinforcement learning problem, resulting in AVs learning to establish coalitions and influence HV behavior through a distributed reward structure that introduces altruism.
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and struggle to understand and adapt to human behavior, which is particularly challenging in mixed autonomy. In a road shared by AVs and HVs, the social preferences or individual traits of HVs are unknown to the AVs and different from AVs, which are expected to follow a policy, HVs are particularly difficult to forecast since they do not necessarily follow a stationary policy. To address these challenges, we frame the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles' interests, and safely adapt to other traffic situations. In contrast with existing works, we quantify AVs' social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and influence the behavior of HVs.